A Comparative Analysis of Augmented Reality Frameworks Aimed at Diverse Computing Applications

Maneli, Mfundo A.; Isafiade, Omowunmi E.

doi:10.23919/ituk56368.2022.10003046

Cited by 1 publication

(1 citation statement)

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“…While the aforementioned research efforts have conducted monumental research on comparing the two different AR frameworks (ARKit and ARCore), none of these research studies has considered the accuracy of AR measurements with four distance criteria, resource utilization as well as run- time of plane detection and mapping under different ambient lighting conditions, using several different mobile devices as presented in this research. In the previous iteration of this work [27], we looked at the two dominating mobile operating systems and compared the AR frameworks they operate on. ARKit and ARCore were the two AR frameworks that were compared, but the comparison was only based on accuracy of AR measurements.…”

Section: Related Workmentioning

confidence: 99%

A Multifactor Comparative Assessment of Augmented Reality Frameworks in Diverse Computing Settings

Maneli

Isafiade

2023

IEEE Access

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Research and development on different augmented reality (AR) frameworks have come a long way when it comes to image tracking, object tracking, plane tracking and light estimation. However, there might be trade-offs and varying results obtained from different AR frameworks, depending on the use cases, and this is critical for consideration during immersive application development. Besides the current literature effort, this research proposes a multifactor comparative analysis of two core AR frameworks, which aims to analyze and evaluate ARKit and ARCore in diverse computing settings. This research developed a structural application which evaluated three major test parameters across ten devices spanning ARKit and ARCore. The first parameter relates to evaluating AR measurements using four different distance criteria. The second parameter evaluated resource utilization, relating to the central processing unit (CPU) and random access memory (RAM), while the last parameter evaluated plane detection based on light estimation. Findings conclude that ARKit is the preferable AR framework for AR measurement accuracy and reliability within the tested distance criteria. ARCore is the most optimized AR framework in terms of RAM utilization. Regarding plane detection based on light estimation, ARCore is the preferable choice under low lighting conditions, however, ARKit is the most suitable AR framework under adequate ambient lighting conditions. The findings of this research could guide future prototyping and immersive mobile application development within the context of the parameters used.

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Section: Related Workmentioning

confidence: 99%